Until recently, only four American companies have a $1 trillion valuation: Alphabet, Amazon, Apple, and Microsoft. That list of technology giants now contains the name of NVIDIA, a company most people know for producing graphics cards (GPU) for personal computers. However, Artificial Intelligence and not traditional GPUs are the driving force behind NVIDIA’s recent meteoric ascendancy into the exclusive “trillion dollar club.”
NVIDIA started life in April 1993 and has since grown into a mega-company employing over 26,000 people globally and has a market capitalization of $1.19 trillion. The company, headquartered in Santa Clara, California, released its latest financial figures to the NASDAQ this week. Those figures reveal NVIDIA enjoyed $13.51 billion in revenue in the second quarter of its 2024 financial year, a whopping 101% increase year-on-year.
Gamers are well versed with the NVIDIA name because the company has long produced some of the best graphics cards in the industry. NVIDIA’s latest graphics cards allow gamers to feast their eyes on vibrant 3D visuals with realistic lighting effects for the same price as the maximum Mybookie welcome bonus awards. Although the graphics card arm of NVIDIA is performing exceptionally well, it is the company’s Data Center division where the bulk of its revenue is generated.
Demand For Artificial Intelligence Hardware
The NVIDIA Data Center division also produces GPUs, but they are different from the kind you find in computers video gamers use. Instead, these powerful data center GPUs accelerate deep learning, machine learning, and high-performance computing required to advance the most complex artificial intelligence.
The data center also features the world’s first portfolio of purpose-built AI Supercomputers, the world’s most powerful server platform, and enterprise software associated with creating and developing artificial intelligence.
To say business is booming in this NVIDIA division is an understatement. The NVIDIA Data Center produced $10.32 billion of revenue in the second quarter of NVIDIA’s 2024 financial year, an increase of 141% from the previous quarter and 171% more than the same period last year.
Jensen Huang, CEO and founder of NVIDIA, explained the race to develop the best artificial intelligence is the driving force behind NVIDIA’s record-breaking revenue.
“A new computing era has begun. Companies worldwide are transitioning from general-purpose to accelerated computing and generative AI. NVIDIA GPUs connected by our Mellanox networking and switch technology and running our CUDA AI software stack make up the computing infrastructure of generative AI.”
“During the quarter, major cloud service providers announced massive NVIDIA H100 AI infrastructures. Leading enterprise IT systems and software providers announced partnerships to bring NVIDIA AI to every industry. The race is on to adopt generative AI.”
NVIDIA Has the AI Processor Market Cornered
NVIDIA has approximately 95% of the GPU market for machine learning, and it has its CEO and founder to thank for that fact. The company focused on making graphics better for gaming and other applications when it launched in 1993.
GPUs are excellent at simultaneously processing many small calculations and tasks, such as handling millions of pixels on a computer screen. Researchers at Stanford University discovered in 2006 that GPUs could be used to accelerate mathematical problems in a way that everyday processors (CPUs) could not.
Jensen set about investing vast resources into creating programmable GPUs. Most gamers were blissfully unaware of the new feature because it did not affect their game’s visuals. Still, it allowed researchers to perform high-performance computing and programming on everyday consumer computers.
Alexnet launched in 2012, six years after the Stanford University researchers’ discovery. Alexnet was an artificial intelligence that could quickly classify images. The software’s developers trained Alexnet using two of NVIDIA’s programmable GPUs, and the training process took less than a week. Those developers estimated training Alexnet would have taken months using regular processors and methods.
Computer scientists learned how amazingly fast the programmable NVIDIA GPUs were at neural network processing, and the surge in demand for the state-of-the-art GPUs began.
The Tools that Trained ChatGPT
November 30, 2022, seems like an innocuous date, but it will one day be remembered as the day artificial intelligence took significant steps to be integrated into the real world. ChatGPT’s initial release was on that day, a powerful AI that gives human-like responses to questions.
The core function of ChatGPT is to mimic human conversation, but it has proven to be much more versatile. Users have reported ChatGPT composing music, generating business ideas, writing poetry and songs, and even debugging computer programs.
The ChatGPT developers and programmers bundled 10,000 of NVIDIA’s powerful GPUs to train the software, each costing between $10,000 and $15,000. Analysts estimate up to 30,000 GPUs could be needed to continue ChatGPT’s development as it learns to become increasingly human.
Could the NVIDIA Bubble Burst?
NVIDIA investors are delighted with the company’s performance, but some financial experts are worried the bubble could suddenly burst, just as it did with other technology companies.
Although NVIDIA currently has the AI-friendly GPU market cornered, other massive semiconductor companies, such as AMD and Intel, are making GPUs dedicated to artificial intelligence applications.
It is not only NVIDIA’s rivals that are competing for market share because Amazon has developed a custom-built chip for training artificial intelligence, while Google created tensor processing units (TPUs) that are used for some machine-learning tasks in addition to providing the company’s famous search engine results.
Then, there are questions regarding whether NVIDIA can continue to keep up with demand for its ground-breaking GPUs. During and after the COVID-19 pandemic, there was a global shortage of semiconductors, which drove prices through the roof. With AMD and Intel now developing their offerings, demand for crucial GPU components will be higher than ever.
Finally, there are also ethical issues surrounding artificial intelligence. Many experts believe NVIDIA should vet products that artificial intelligence creators are producing, while concerns about the impact of powerful AI on human society refuse to go away.